Translation quality assurance based on large language models

This disclosure relates generally to the field of machine learning, and more particularly to large language models.

Machine translation (MT) uses either rule-based or probabilistic machine learning approaches to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Traditionally, machine translation has used statistical methods but has recently adopted neural network-based approaches. Machine translation has been widely used to lower costs associated with translation of textual materials.

Embodiments relate to a method, system, and computer program product for assuring quality of machine translations based on large language models. According to one aspect, a method for assuring quality of machine translations based on large language models is provided. The method may include receiving data corresponding to an input in a first language for machine translation. The received data is translated from the first language to a second language through a translation engine. A confidence value associated with the translated data is determined. The translation is revised with executable source code based on the confidence value being greater than a threshold value. Otherwise, the translation is revised based on sending a prompt to a large language model based on the confidence value being less than the threshold value.

According to another aspect, a computer system for assuring quality of machine translations based on large language models is provided. The computer system may include one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, whereby the computer system is capable of performing a method. The method may include receiving data corresponding to an input in a first language for machine translation. The received data is translated from the first language to a second language through a translation engine. A confidence value associated with the translated data is determined. The translation is revised with executable source code based on the confidence value being greater than a threshold value. Otherwise, the translation is revised based on sending a prompt to a large language model based on the confidence value being less than the threshold value.

According to yet another aspect, a computer program product for assuring quality of machine translations based on large language models is provided. The computer program product may include one or more computer-readable storage devices and program instructions stored on at least one of the one or more tangible storage devices, the program instructions executable by a processor. The program instructions are executable by a processor for performing a method that may accordingly include receiving data corresponding to an input in a first language for machine translation. The received data is translated from the first language to a second language through a translation engine. A confidence value associated with the translated data is determined. The translation is revised with executable source code based on the confidence value being greater than a threshold value. Otherwise, the translation is revised based on sending a prompt to a large language model based on the confidence value being less than the threshold value.

Detailed embodiments of the claimed structures and methods are disclosed herein; however, it can be understood that the disclosed embodiments are merely illustrative of the claimed structures and methods that may be embodied in various forms. Those structures and methods may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete and will fully convey the scope to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of machine learning, and more particularly to large language models. The following described exemplary embodiments provide a system, method, and computer program product to, among other things, ensure the quality of machine translations confidence values using large language models based on confidence values associated with the translation. Therefore, some embodiments have the capacity to improve the field of computing by allowing for machine translation of speech and text while minimizing the need for human post-editing of such translations.

As previously described, machine translation (MT) uses either rule-based or probabilistic machine learning approaches to translate text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. Traditionally, machine translation has used statistical methods but has recently adopted neural network-based approaches. Machine translation has been widely used to lower costs associated with translation of textual materials.

However, although machine translation quality is much better now with newer technologies, there are still some scenarios and cases in which machine translation results are not perfect. These may include, among other things, term translation or inline tag related format issues. For machine translation quality issues, human post-editing is one way to achieve a better translation result. However, this is expensive and requires a longer turnaround time. In addition, similar issues will re-occur when new content is translated with same machine translation engine, which may further introduce a poor user experience and increased translation costs.

It may be advantageous, therefore, to use a Large Language Model (LLM) based self-learning and intelligent solution to help improve and guarantee the translation quality, especially for machine translation results. Such solution may allow for auto-collection of low quality translation cases from user tickets or human post-editing history and create groups for different types of translation issues. Self-learning prompts may be composed with pre-defined templates and collected samples, so that verification testing may be used to check and tune the learning effects. Learned knowledge may be converted into executable source code for groups with good learning effects in order to check and improve the source code maturity with regression test. The system may then intelligently select the proper choice for new cases to improve translation quality within reasonable response time.

Thus, the method, computer system, and computer program product disclosed herein may be used to achieve better translation results with less human intervention and lower translation cost. The disclosed method, computer system, and computer program product may handle many translation cases with auto-generated source code, reduce the calls to large language models, mitigate challenges of large language model response latency, and process heavy workloads with acceptable turnaround time. The disclosed method, computer system, and computer program product may support self-serve translation review/correction that may help improve user satisfaction rate and user experience.

Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.

A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.

The following described exemplary embodiments provide a system, method and computer program product that improves machine translation based on comparing translation instances needing review to known good translations. Referring now to, Computing environmentcontains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as Translation Quality Assurance. In addition to Translation Quality Assurance, computing environmentincludes, for example, computer, wide area network (WAN), end user device (EUD), remote server, public cloud, and private cloud. In this embodiment, computerincludes processor set(including processing circuitryand cache), communication fabric, volatile memory, persistent storage(including operating systemand Translation Quality Assurance, as identified above), peripheral device set(including user interface (UI) device set, storage, and Internet of Things (IoT) sensor set), and network module. Remote serverincludes remote database. Public cloudincludes gateway, cloud orchestration module, host physical machine set, virtual machine set, and container set.

COMPUTERmay take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer or any other form of computer or mobile device now known or to be developed in the future that is capable of running a program, accessing a network or querying a database, such as remote database. As is well understood in the art of computer technology, and depending upon the technology, performance of a computer-implemented method may be distributed among multiple computers and/or between multiple locations. On the other hand, in this presentation of computing environment, detailed discussion is focused on a single computer, specifically computer, to keep the presentation as simple as possible. Computermay be located in a cloud, even though it is not shown in a cloud in. On the other hand, computeris not required to be in a cloud except to any extent as may be affirmatively indicated.

PROCESSOR SETincludes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitrymay be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitrymay implement multiple processor threads and/or multiple processor cores. Cacheis memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor setmay be designed for working with qubits and performing quantum computing.

Computer readable program instructions are typically loaded onto computerto cause a series of operational steps to be performed by processor setof computerand thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cacheand the other storage media discussed below. The program instructions, and associated data, are accessed by processor setto control and direct performance of the inventive methods. In computing environment, at least some of the instructions for performing the inventive methods may be stored in Translation Quality Assurancein persistent storage.

COMMUNICATION FABRICis the signal conduction path that allows the various components of computerto communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.

VOLATILE MEMORYis any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memoryis characterized by random access, but this is not required unless affirmatively indicated. In computer, the volatile memoryis located in a single package and is internal to computer, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer.

PERSISTENT STORAGEis any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computerand/or directly to persistent storage. Persistent storagemay be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and re-writing of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating systemmay take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface-type operating systems that employ a kernel. The code included in Translation Quality Assurancetypically includes at least some of the computer code involved in performing the inventive methods.

PERIPHERAL DEVICE SETincludes the set of peripheral devices of computer. Data communication connections between the peripheral devices and the other components of computermay be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion-type connections (for example, secure digital (SD) card), connections made through local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device setmay include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storageis external storage, such as an external hard drive, or insertable storage, such as an SD card. Storagemay be persistent and/or volatile. In some embodiments, storagemay take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computeris required to have a large amount of storage (for example, where computerlocally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor setis made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

NETWORK MODULEis the collection of computer software, hardware, and firmware that allows computerto communicate with other computers through WAN. Network modulemay include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network moduleare performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network moduleare performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computerfrom an external computer or external storage device through a network adapter card or network interface included in network module.

WANis any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WANmay be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.

END USER DEVICE (EUD)is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer), and may take any of the forms discussed above in connection with computer. EUDtypically receives helpful and useful data from the operations of computer. For example, in a hypothetical case where computeris designed to provide a recommendation to an end user, this recommendation would typically be communicated from network moduleof computerthrough WANto EUD. In this way, EUDcan display, or otherwise present, the recommendation to an end user. In some embodiments, EUDmay be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

REMOTE SERVERis any computer system that serves at least some data and/or functionality to computer. Remote servermay be controlled and used by the same entity that operates computer. Remote serverrepresents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer. For example, in a hypothetical case where computeris designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computerfrom remote databaseof remote server.

PUBLIC CLOUDis any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloudis performed by the computer hardware and/or software of cloud orchestration module. The computing resources provided by public cloudare typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set, which is the universe of physical computers in and/or available to public cloud. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine setand/or containers from container set. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration modulemanages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gatewayis the collection of computer software, hardware, and firmware that allows public cloudto communicate through WAN.

Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.

PRIVATE CLOUDis similar to public cloud, except that the computing resources are only available for use by a single enterprise. While private cloudis depicted as being in communication with WAN, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloudand private cloudare both part of a larger hybrid cloud.

Referring now to, a functional block diagram of a networked computer environment illustrating a digital translation assurance system(hereinafter “system”) for assuring quality of machine translations based on large language models. It should be appreciated thatprovides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

The systemmay include a computerand a server computer. The computermay communicate with the server computervia a communication network(hereinafter “network”). The computermay include a processorand a software programthat is stored on a data storage deviceand is enabled to interface with a user and communicate with the server computer. The computermay be, for example, a mobile device, a telephone, a personal digital assistant, a netbook, a laptop computer, a tablet computer, a desktop computer, or any type of computing devices capable of running a program, accessing a network, and accessing a database.

The server computer, which may be used for assuring quality of machine translations based on large language models is enabled to run a Digital Translation Assistant Program(hereinafter “program”) that may interact with a database. The Digital Translation Assistant Program is explained in more detail below with respect to. In one embodiment, the computermay operate as an input device including a user interface while the programmay run primarily on server computer. In an alternative embodiment, the programmay run primarily on one or more computerswhile the server computermay be used for processing and storage of data used by the program. It should be noted that the programmay be a standalone program or may be integrated into a larger digital translation assistant program.

It should be noted, however, that processing for the programmay, in some instances be shared amongst the computersand the server computersin any ratio. In another embodiment, the programmay operate on more than one computer, server computer, or some combination of computers and server computers, for example, a plurality of computerscommunicating across the networkwith a single server computer. In another embodiment, for example, the programmay operate on a plurality of server computerscommunicating across the networkwith a plurality of client computers. Alternatively, the program may operate on a network server communicating across the network with a server and a plurality of client computers.

The networkmay include wired connections, wireless connections, fiber optic connections, or some combination thereof. In general, the networkcan be any combination of connections and protocols that will support communications between the computerand the server computer. The networkmay include various types of networks, such as, for example, a local area network (LAN), a wide area network (WAN) such as the Internet, a telecommunication network such as the Public Switched Telephone Network (PSTN), a wireless network, a public switched network, a satellite network, a cellular network (e.g., a fifth generation (5G) network, a long-term evolution (LTE) network, a third generation (3G) network, a code division multiple access (CDMA) network, etc.), a public land mobile network (PLMN), a private network, an ad hoc network, an intranet, a fiber optic-based network, or the like, and/or a combination of these or other types of networks.

The number and arrangement of devices and networks shown inare provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in. Furthermore, two or more devices shown inmay be implemented within a single device, or a single device shown inmay be implemented as multiple, distributed devices. Additionally, or alternatively, a set of devices (e.g., one or more devices) of systemmay perform one or more functions described as being performed by another set of devices of system.

Referring now to, a block diagram of a translation assurance systemis depicted according to one or more embodiments. The translation assurance systemmay include, among other things, a machine translation module, a collection module, a learning and verification module, and a source code generation and testing module.

The machine translation modulemay be a translation engine that may translate input translation datafrom a first language to another. The input translation datamay be text or audio data. The machine translation data may receive a translation request from a user of the software program() on the computer() or the Digital Translation Assistant program() on the server computer().

The collection modulemay be, among other things, a large language model (LLM) or other machine learning module. The collection modulemay automatically collect data corresponding to low quality translation cases. For example, the collection modulemay gather data from user tickets or from post-editing history from human translations. The collection modulemay, for example, extract an issue description from a user ticket, such as an incorrect word being used in place of a homograph. The collection modulemay summarize a translation issue by identifying a given translation issue from a user ticket or post-editing history and extract examples including a sentence in a first translation, a reported poor translation, and a user-suggested correct translation based on script template data. The collection modulemay group user tickets and post-edit history data into several categories based on the summarized translation issue.

The learning and verification modulemay learn from the translation issue data gathered by the collection modulein order to determine correct translations for cases needed correction or for which corrected translations have not been provided. The learning and verification modulemay use the information learned from previous cases to generate a correct translation. Because self-learning may require several iterations, the learning and verification module may determine how many samples may be needed for learning based on verification of the accuracy of the translations. The learning and verification modulemay verify the accuracy of the translations based on comparing proposed translations to known correct translations in order to determine whether the test case has passed. Based on the testing result, a leaning confidence score may be recorded for each group to indicate if the machine translation modulemay be able to successfully handle cases in each group. If the verification testing score is lower than a pre-defined threshold value, the learning and verification module may identify a need for more samples to be provided for further learning.

The source code generation and testing modulemay generate and update source code for use in translation of the input translation data. The source code generation and testing modulemay check corrected translations generated by the learning and verification moduleand may summarize a generalized set of rules applicable to the corrected translations for generation as source code. The source code generation and testing modulemay perform regression testing on the generated source code against one or more test cases. Such testing may include, among other things, whether each and every word is translated, appropriateness of punctuation, consistency of spaces, and preservation of special formatting. If the regression testing exceeds a threshold value, the source code is deemed acceptable for use in future machine translations. If the regression testing does not exceed the threshold value, then the source code generation and testing modulemay be asked to re-generate source code with additional information corresponding to failed translation cases. A score may be recorded for each group's source code.

Thus, by providing several translation examples for digital translation, the translation assurance systemmay be able to get better machine translation results for new cases. Machine translation users may, therefore, no longer suffer from similar translation issues, and the user experience and satisfaction can be highly improved. For professional translators, the translation assurance systemmay learn from ongoing post-editing results, auto-apply the learned knowledge, and revise the translations of following segments in order to improve the translation efficiency and consistency during the post-editing process.

Referring now to, an operational flowchart illustrating the steps of a methodcarried out by a program that assures quality of machine translations based on large language models is depicted. The methodmay be described with the aid of the exemplary embodiments of.

At, the methodmay include receiving data corresponding to an input in a first language for machine translation. The received data corresponds to textual or audio data. In operation, the machine translation module() may receive input translation data() from the software program() on the computer() or from the Digital Translation Assistant Program() on the server computer().

At, the methodmay include translating the received data from the first language to a second language through a translation engine to generate a translation of the received data. The translation engine is updated in response to one or more user tickets corresponding to issues associated with translation of the received data. In operation, the machine translation module() may translate the input translation data() to a new language. The collection module() may gather cases of poor translation from the machine translation moduleand may pass information on such cases, along with user tickets and post-editing history data, to the learning and verification module() in order to train the machine translation module.

At, the methodmay include determining a confidence value associated with the translated data. The translation engine is trained based on comparing one or more translation cases identified for review with one or more known correct translations based on confidence values associated with the threshold value. Comparing the one or more translation cases identified for review with the one or more known correct translations corresponds to determining whether confidence values for the one or more translation cases identified for review and the one or more known correct translations are greater than the threshold value. In operation, the collection module() may gather cases of poor translation and group them into one or more groups. The learning and verification module() may compare the groups to known good translation cases in order to improve the translation quality of the machine translation module().

At, the methodmay include revising the translation with executable source code based on the confidence value being greater than a threshold value. The source code is updated based on the trained translation engine. In operation, the source code generation and testing module() may revise the source code based on a confidence value associated with the quality of source code translation being less than a pre-determined threshold value. The source code generation and testing modulemay pass this source code to the machine translation module() for improving quality of translation of the translation input data().

At, the methodmay include revising the translation based on sending a prompt to a large language model based on the confidence value being less than the threshold value. A second confidence value associated with the revising of the translation based on sending the prompt to the large language model is determined. A need for human post-editing is identified based on the second confidence value being less than a second threshold value. In operation, the source code generation and testing module() may determine that the quality of even the source code translation may be insufficient and may pass the translation of the input translation data() to a large language model for improving the translation.

It may be appreciated thatprovides only an illustration of one implementation and does not imply any limitations with regard to how different embodiments may be implemented. Many modifications to the depicted environments may be made based on design and implementation requirements.

Some embodiments may relate to a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer-readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out operations.

The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.